This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B, enhanced with instruction-tuned chain-of-thought (CoT) reasoning across three problem domains: math, text-to-SQL, medical-reasoning, and Python programming.

Fine-tuning was conducted using DeepSpeed on a multi-A100 GPU setup via RunPod for efficient training in memory-constrained environments. The training dataset includes CoT-formatted tasks with natural language questions and structured reasoning paths.

✅🔥Inference notebook is publicly available 👉 here.

📎 Model Details

Base Model: eagle0504/qwen-distilled-scout-1.5b-instruct-gen1
Language: English
Architecture: Causal Language Model (Decoder-only)
Tokenizer: AutoTokenizer from base model
Parameter Count: 1.5 Billion
Training Framework: 🧢 Transformers + DeepSpeed
Compute Environment: RunPod (6x A100 SXM, 192 vCPU, 1.5TB RAM)

🧪 Training Dataset

Datasets Used:

Each example in the dataset follows the structure:

xml

<instruction>This is a [math/SQL/Python/medical] problem.</instruction>
<question>...</question>
<think>...</think>
<response>...</response>

This instruction format ensures that the model understands the task type explicitly and applies step-by-step reasoning across all domains.

📊 Fine-Tuning Summary

The base model deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B was fine-tuned on three different datasets using DeepSpeed across various RunPod infrastructure setups. Below is a consolidated summary of the training configurations and results:

Model ID	Dataset Description	GPUs	vCPUs	RAM (GB)	Disk per GPU	Container Image	Duration	Cost	Total Cost	DeepSpeed Stage	Precision	Mean Token Accuracy
`eagle0504/openai-gsm8k-enhanced-using-together-ai-deepseek-train8k-test1k-v1`	OpenAI GSM8K Enhanced v2	6 × H100 PCIe	144	1132	20 GB	`runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04`	3 hrs	~$14	~$42	Stage 1	FP16	98%
`eagle0504/augmented_codealpaca-20k-using-together-ai-deepseek-v1`	GSM8K + CodeAlpaca-20K Enhanced	4 × A100 SXM	146	1144	20 GB	`runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04`	3 hrs	~$7+	~$21+	Stage 1	FP16	98%
`gretelai/synthetic_text_to_sql`	Custom CoT + SQL-Reasoning	6 × A100 SXM	192	1536	20 GB	`runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04`	2.5 hrs	~$21	~$52.5	Stage 2	FP16	97%
`FreedomIntelligence/medical-o1-reasoning-SFT`	CoT + Medical-Reasoning	4 x A100 SXM	146	1144	20 GB	`runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04`	17 hrs	~$7+	~$119	Stage 2	FP16	99%

🏗️ Training Configuration

Training was performed with the following configuration:

Batch Size: 2 (with gradient accumulation steps = 4)
Epochs: 15
Max Length: 1024 tokens
Optimizer: AdamW
Learning Rate: 5e-5 (with warmup + linear decay)
Precision: FP16
DeepSpeed Config:
- Zero Redundancy Optimizer Stage 2
- Gradient Clipping: 1.0
- AllGather + ReduceScatter optimization
Checkpoint Saving: Disabled to minimize disk usage

🧶 Evaluation Metric

The model is evaluated with a custom token-level accuracy metric:

Metric: Mean token-level accuracy
Definition: Accuracy over all non-masked tokens (labels != -100)
Implementation: NumPy-based vectorized comparison between predicted tokens and ground truth

🚀 Use Case

This model is tuned for instruction-driven chain-of-thought generation, and is especially useful in:

Educational tools for logical reasoning and coding
Auto SQL and code generation for tabular or structured systems
Teaching agents in math, database, and programming domains
Conversational agents requiring task-specific structured outputs
Medical diagnosis reasoning

📦 How to Use

python

from transformers import StoppingCriteria, StoppingCriteriaList
import torch

class StopOnTokens(StoppingCriteria):
    def __init__(self, stop_token_ids: list):
        super().__init__()
        self.stop_token_ids = stop_token_ids

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        return any(input_ids[0, -len(token):].tolist() == token for token in self.stop_token_ids)

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("eagle0504/qwen-distilled-scout-1.5b-instruct-gen1")
tokenizer = AutoTokenizer.from_pretrained("eagle0504/qwen-distilled-scout-1.5b-instruct-gen1")

stop_sequence = "</response>"
stop_ids = tokenizer.encode(stop_sequence, add_special_tokens=False)
stopping_criteria = StoppingCriteriaList([StopOnTokens([stop_ids])])

# Choose amongst math, SQL, python, or medical in the instruction.
prompt = (
    "<instruction>This is a [math, SQL, python, medical] problem.</instruction>"
    "<question>Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether?</question>"
)

inputs = tokenizer(
    prompt,
    return_tensors="pt"
)

outputs = model.generate(
    **inputs,
    max_new_tokens=1024, # use max token limit and this may not be needed because stop word is set up above
    stopping_criteria=stopping_criteria # stop word is in place so we may not need all 1024 tokens
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📊 Limitations

The model is specialized for instruction-following tasks in math, SQL, medical and Python reasoning. It may require further fine-tuning to generalize to open-domain dialogue or creative generation.
Input length is capped at 1024 tokens, beyond which content will be truncated.

🧑‍💻 Author

Name: Yiqiao Yin
Hugging Face: eagle0504

📝 Citation

bibtex

@misc{yin2025instructgen1,
  title={Instruction-Tuned Qwen 1.5B Fine-tuned on Math + SQL + Python + medical CoT Tasks},
  author={Yiqiao Yin},
  year={2025},
  howpublished={\url{https://huggingface.co/eagle0504/qwen-distilled-scout-1.5b-instruct-gen2}},
}